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Authors: Tomi Suhonen1,*, Anssi Laukkanen1, Tom Andersson1, Tatu Pinomaa1, Kenneth Holmberg1
1 VTT Technical Research Centre of Finland, Kemistintie 3, 02044 VTT, Espoo, Finland
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Development of new materials and understanding of material and process behaviour is always a complex equation of crossing interactions. Physical and chemical phenomena are affected from the nano- and/or molecular level up to macroscopic level. Interactions between the material processing, structure, properties and performance (PSPP) need to be understood more deeply. For this purpose, modelling skills have developed rapidly in recent decades, with the support of increased numerical calculation capacity and commercial, open source and in-house multi-level and multi-physics software development. In this publication we are presenting some highlights from our current modelling activities obtained wit VTT ProperTune concept related to powder metallurgical (PM) and additively manufactured (AM) materials. We hope they will inspire new ideas on what could be done and obtained via digital approach to design.

Computation-driven design of hard and PM materials

The mesoscalemodelling concept of current work exploits the generally acknowledged PSPP approach in utilising modelling and simulation for material design problems, adapted for PM materials and presented in more detail in Holmberg et al.1,2. At the core are the linkages between material characteristics that can be systematically built and investigated by developing models between the different steps of the PSPP paradigm. The resulting correlations can be studied to quantitatively grasp the significance of nano-microstructural material features and physical phenomena giving rise to material properties and product performances. These approaches enable the effective exploitation of computation and multiscale materials modelling in design of composite materials. The implementation of these means in practice falls within the realm of integrated computational materials engineering (ICME), where computation merges with experimental, characterisation and material informatics to introduce a general and practically exploitable concept for simulation-driven material design. For hard and PM materials in the implementation of PSPP we have identified three common steps, or specific problems typically tackled and found to be of interest, primarily due to the goal of exploiting modelling as a part of a performance-driven material design chain. These can be viewed as common use case problems on how to exploit multiscale modelling e.g. in design of PM materials, material selection or solving specific material-related problems. First, in Fig. 1, the structure–property problem is presented and case examples briefly outlined for various hard material systems and affiliated microstructures. In a structure–property problem the modelling problem is centred around the digital representation of material nano-microstructure and phenomena responsible for basic material properties, such as strength, viscoplastic strain rate, hardness, etc. As such, the key element is in possessing the capability to generate realistic enough representation of material structure, such as reinforcing constituents and defect structure, along with e.g. the underlying metallic microstructure.

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Figure 1.Modelling in solving the structure to property problem for PM materials


The most common problem types are affiliated with deformation response and following properties such as compressive strength, deciphering what features in material structure are critical for strengthening of the system and how to systematically work towards desired material properties. As such, the computation
takes the form of simulating and carrying out virtual testing of common laboratory experiments such as hardness, compression and scratch testing, and the models are applied to investigate the correlation between structural morphologies, mechanisms of deformation and the resulting material properties. The structure–property–performance problem can be viewed as the next step in exploiting computation for design of hard and PM materials, and such a concept and case example is presented in Fig. 2. Performance as a term is typically affiliated with more elaborate properties than those related to material properties, such as resistance to fatigue, wear or fracture toughness (see e.g. Holmberg et al.1,2 for a more thorough discussion and further references). Subsequently, performance by the convention adopted by current authors pertains to behaviour also including component design, the use of the material in its operating conditions and environments. As such, the structure–property–performance can also be identified as an extension of the structure–property problem to more generic conditions. In Fig. 2, a case example of fatigue evaluation of selective laser melting precipitation hardened microstructure of steel is given. The differences to make note of in comparison to the structure–property problem are that material defect structure, criteria for failure micromechanisms or explicit modelling of failure processes and the conditions under which the product operates are introduced in modelling the problem. In Fig. 2, the analysis proceeds by merging a microstructural analysis of how a defect structure responds under deformation during fatigue cycling, and the subsequent stress–strain response is utilised to carry out a microstructure informed fatigue analysis. The merits of such an analysis lie in the fact that individual microstructural defects can be quantitatively linked to fatigue initiation life, enabling one to for example evaluate the significance of specific microstructural features on fatigue life, or whether certain specific defect types are at all of relevance for operational life of a component.

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Figure 2. Modelling in solving the structure, property to performance problem for PM materials

The links to e.g. certification and developing PM parts for extreme performance environments are imminent in addition to the general notions presented for performance-driven material design. As the third and last use case type for modelling-driven material design, the PSPP analysis type for PM materials is presented in Fig. 3. The use case can be viewed from the perspective of the structure–property–performance problem by introducing simulation of the material processing and manufacturing step, i.e. the analysis can now treat for example solidification and sintering structures directly, rather than use characterisation or like information in generating the computer-generated representation of material structure. The analysis complexity increases, but the potential impact does so as well. For example, in Fig. 3, the case depicts a PSPP analysis for additive manufacturing (AM). The process model comprises of a thermomechanical FE solver, which in current case is an example run on eight different process parameter sets to yield differing thermal histories.

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Figure 3. Modelling in solving the process, structure and property to performance problem for PM materials

The output of such a thermomechanical analysis can be used in a PF analysis for alloy solidification, producing the resulting nano-microstructure. The structure– property–performance problem henceforth is carried out similarly as in other analysis scenarios. The merit of the PSPP analysis chain is in enabling the linking of material processing parameters and variables all the way to component performance. This enables the systematic investigation of causal relations accounting for all critical stages from material and component manufacturing to performance of the product, and these relations can then be systematically exploited in design of products with the required performances and also in evaluating the cost-effective means of delivering those performances and developing improved materials.

 

1. K. Holmberg, A. Laukkanen, A. Ghabchi, M. Rombouts, E. Turunen, R. Waudby, T. Suhonen, K. Valtonen and E. Sarlin: ‘Computational modeling based wear resistance analysis of thick composite coatings’, Tribol. Int., 2014, 72, 13–30.

2. K. Holmberg, A. Laukkanen, E. Turunen and T. Laitinen: ‘Wear resistance optimization of composite coatings by computational microstructural modeling’, Surf. Coat. Tech., 2014, 247, 1–13.